Shubhamsaboo/adk-samples
Jun 2025 – Jun 2025
1 Month active
Languages Used
MarkdownPython
Technical Skills
AI Agent DevelopmentNatural Language ProcessingPrompt EngineeringResearch Automation
Deepak moonat
PROFILE
Deepak Moonat
During June 2025, Deepak Moonat enhanced the Gemini-Fullstack Agent in the Shubhamsaboo/adk-samples repository by refining its research planning and execution capabilities. He focused on prompt engineering and natural language processing to improve plan generation, information gathering, and synthesis phases. Deepak introduced a structured tagging taxonomy to classify research tasks and deliverables, which increased repository-level visibility and collaboration. His work, implemented in Python and Markdown, centered on strengthening instructions for output creation and research workflows. The depth of these changes resulted in more accurate planning, better traceability, and faster, more cohesive deliverables, addressing key challenges in research automation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
87
Activity Months1
Your Network
47 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 summary of key accomplishments in Shubhamsaboo/adk-samples: Delivered enhancements to the Gemini-Fullstack Agent, including refined prompts for research planning and execution, and a new tagging taxonomy to classify research tasks and deliverables. Strengthened instructions for plan generation, refinement, and output creation, with explicit guidance for information gathering and synthesis phases. Code changes focused on prompt refinement (commit 521f24cf7ccb7965655fc5035fc997c2e7af1e8c). Resulted in improved planning accuracy, better traceability, and faster, more cohesive deliverables, enabling stronger collaboration and business value.
1 Commits • 1 Features
Jun 1, 2025June 2025 summary of key accomplishments in Shubhamsaboo/adk-samples: Delivered enhancements to the Gemini-Fullstack Agent, including refined prompts for research planning and execution, and a new tagging taxonomy to classify research tasks and deliverables. Strengthened instructions for plan generation, refinement, and output creation, with explicit guidance for information gathering and synthesis phases. Code changes focused on prompt refinement (commit 521f24cf7ccb7965655fc5035fc997c2e7af1e8c). Resulted in improved planning accuracy, better traceability, and faster, more cohesive deliverables, enabling stronger collaboration and business value.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage90.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AI Agent DevelopmentNatural Language ProcessingPrompt EngineeringResearch Automation
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